AGILE AND AUTOMATION CONCLAVE 2018 - Accenture · 2018-07-03 · Agile and Automation Conclave 2018...
Transcript of AGILE AND AUTOMATION CONCLAVE 2018 - Accenture · 2018-07-03 · Agile and Automation Conclave 2018...
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AGILE AND AUTOMATION CONCLAVE 2018
FUTURE OF ENTERPRISE AICHALLENGES AND OPPORTUNITIES
JANARDAN MISRA
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Agile and Automation Conclave 2018
JANARDAN MISRATECHNOLOGY RESEARCH SR. PRINCIPAL, ACCENTURE LABS• 18+ years of R&D experience with contributions in areas of Unstructured
Data Analytics, Information Retrieval, Applied Machine Learning, and Complex
Adaptive Systems.
• 30+ peer reviewed research papers and a monograph.
• 40+ patents (issued and pending) across multiple geographies.
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Agile and Automation Conclave 2018
AGENDA
• ENABLING TECHNOLOGIES
• WORKFORCE IMPLICATIONS
• POTENTIAL PITFALLS
• CURRENT STATE OF AI
• CHALLENGES WITH TODAY’S AI
• EMERGING TECHNIQUES
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Agile and Automation Conclave 2018
CURRENT STATE OF AI
What can AI Do?
“If a typical person can do a mental task with < 1 second of thought, we can probably automate it using AI either now or in the near future”
-- Andrew Ng
• Often most helpful in complex environments
Probable Futures
Information
AI
Funnel
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Agile and Automation Conclave 2018
AI – CURRENT STATE (CONT.)
• Major Breakthroughs
• Key Paradigm
• Deep Neural Networks based Supervised Learning
Speech and Image Recognition Language TranslationPersonalized Recommendations Credit Card Fraud DetectionSpam Filtering Search
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Agile and Automation Conclave 2018
CHALLENGES WITH TODAY’S AI
Data Challenges• Effectiveness may come only with millions of data-points • Difficult to create ‘gold standard’ data set for training and validation
Engineering Challenges• Software Engineering for AI is still evolving! • Difficult to debug and incrementally improve in contrast to classical programming
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Agile and Automation Conclave 2018
CHALLENGES WITH TODAY’S AI (CONT.)
Functional Challenges
• Causal Inferencing• Learning causation beyond correlations
Are these two definitions equivalent?• “A number that is divisible only by
itself and 1” • “a natural number greater than 1
that cannot be formed by multiplying two smaller natural numbers”
Temperature and Ice-cream sales are correlated!
• Do high temperatures cause high sales or vice versa?
• Reasoning• Commonsense and open-ended
inferences• Comprehension
• Learning abstractions through definitions
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Agile and Automation Conclave 2018
EMERGING TECHNIQUES
COMPOSABLE AI SYSTEMS• Model vs Action
Composition
AI-SPECIFIC ARCHITECTURES• Domain Specific
ML models and hardware
NATURAL LANGUAGE PROCESSING• Conversational
and Q&A Agents
DEEP LEARNING + BIG DATA• Ability to learn
indefinitely as more data comes in
MISSION-CRITICAL AI• Acting in
Dynamic environments
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Agile and Automation Conclave 2018
ENABLING TECHNOLOGIES
Never-ending Active Learning• Continuously learn as you predict with human-in-the-loop
Transfer Learning • Knowledge Reuse• Example: To be able to learn on open data to solve closed enterprise problems
Unsupervised Learning• Learning autonomously without explicit training
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Agile and Automation Conclave 2018
WORKFORCE IMPLICATIONS
Data
Prediction
Judgement
Action
Output
Feedback
Employing Prediction Machines
• AI trainers vs designers
• Creative thinking vs routine execution
Experts-in-the-loop
• Complexity of judgements will be deciding
factor
• Skills to make right judgements will be critical
for the future workforces
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Agile and Automation Conclave 2018
POTENTIAL PITFALLS
Technical Debt
• Potentially high maintenance costs after quick design wins
Lack of Explainability
• Most successful AI techniques are opaque
• “Right to Explanation” – GDPR
Security Concerns
• Data Poisoning attacks
• Lack of robustness against adversaries
Ethical Concerns
• How to ensure fairness?
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Agile and Automation Conclave 2018
REFERENCESBooks• Human + Machine: Reimagining Work in the Age of AI. Paul Daugherty and H. James Wilson, Harvard
Business Review Press 2018
• The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Pedro
Domingos, Basic Books, 2018
• Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence. Jerry Kaplan,
Yale University Press, 2015
Articles• Artificial Intelligence and Life in 2030: One Hundred Year Study on Artificial Intelligence. Peter Stone et
al., Stanford University, 2016
• A Berkeley View of Systems Challenges for AI. Ion Stoica et al., arXiv.org, 2017
• What Artificial Intelligence Can and Can’t Do Right Now. Andrew Ng, HBR, 2016
• Future progress in Artificial Intelligence: A Survey of Expert Opinion. V. C. Müller and N. Bostrom, Springer
2016
• Deep Learning: A Critical Appraisal. Gary Marcus, arXiv.org, 2018
• What can Machine Learning Do? Workforce Implications. Eric B. and Tom Mitchell, Science, 2017
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Agile and Automation Conclave 2018
Q&A
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Agile and Automation Conclave 2018
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Thank you!
Janardan [email protected]